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Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

Technology growth in facebook, whatsapp, instagram has become famous and widely used in diverse social media groups. The intention of facebook is to make the universe widely open and to stay connected. The recent mission of facebook is to stay connected with friends, colleagues, family through sharing photos, videos, stories etc. to show the daily events in the world, which provides the mean to share and express the feelings on what matters to them. Cross platform Instant messaging applications like Telegram and WhatsApp Messenger that smart phone users to establish a ubiquitous technology to exchange text, image, video and audio messages for free. This flexible technology explores more opportunities for risk and benefit for the modern era. Terrorism has increased in certain fragile parts of the world and they attack remotely. New technologies like Artificial intelligence, Machine learning autonomous and semi-autonomous systems are used by terrorist group to attack the network. These groups use social media weapons like facebook, whatsapp, Instagram to spread their information on the social network. It is essential to detect, pre-empt, prevent and eliminate the terrorism through technological spear. Terrorist groups are utilizing the internet as a platform together and convince the sin less people to take part in terrorist activities by infuriating them through web pages that inspired is enchanted individuals to take part in terrorist activities. The detection of terrorist activities needs enormous human effort. To reduce the human effort, our implement system detects the terrorist groups in social media using data mining algorithm. The intention of this work is to is to reduce the terrorism spread and to remove the terrorism related accounts effortlessly.

Collection of information is available in cloud and growing at an exponential rate and this scope makes the organisation and accessing of data critical for efficient use of knowledgeable information. In order to obtain the desired outcome, users heavily rely upon the cloud information retrieval methods. Many research works have been conducted in the field of knowledge related information retrieval. However, effective ranking of complex query pose significant challenges. In this paper, to efficiently rank the query on cloud services a method called, weighted fuzzy multi-keyword rank query (WFM-KRQ) is introduced. This method is based on associating a query with a fuzzy rule, applying a weighted distribution function and a pruning technique based on a threshold value. The proposed encrypted RepeatKeyRotate algorithm effectively improves the performance rate of cloud data sharing services by reducing the processing time and improving the retrieval rate on handling complex queries when compared to the state-of-the-art works.